Computer networks, such as public networks (e.g., the Internet) and private networks (e.g., at medical institutions, financial institutions, business enterprises, etc.) have become a medium for research, communication, distribution, and storage of data. Consequently, more and more devices are network-enabled. To illustrate, on any given day, a typical user may access a half-dozen or more network-enabled devices, such as their mobile phone, tablet computer, home security system devices, one or more wearable devices, a home laptop or desktop, a work laptop or desktop, and home entertainment devices (e.g., televisions, game consoles, set top boxes, etc.). Moreover, Internet of Things (IoT) protocols enable network-enabled devices to communicate with each other without user intervention. Thus, there is an increasing amount of data being accessed, transferred, and stored online. As users use networks to access data, they also generate a large amount of data regarding themselves. On websites such as social networks, users actively and willingly share data regarding themselves. Analyzing such large data sets in a timely fashion may be difficult. In addition, because the data sets may be stored in multiple data structures (e.g., tables), querying the data sets may include performing lookup operations on various data structures, which may be inefficient.